•  
  •  
 

Coal Geology & Exploration

Abstract

In automatic detection of coal petrology by image analysis technology, it is found that the boundary of various components in the microscopic image of coal petrology demonstrates a "rim" of gray transition zone, and the pixel of this rim cannot reflect the true gray level of the components on both sides. In order to analyze its influence on the identification and detection of macerals, the characteristics and causes of the false boundary between various adjacent components were studied with analysis of microscopic images collected from a large number of coal samples in China. Generally, the false boundary is ring-shaped or strip-like rim, showing the gray ramp characteristics in-between the gray levels of adjacent components. The width of gray ramp of the false boundary is related to the combination of various components in microscopic images. False boundary develops by different heights of the relief due to different hardness and toughness of various components in the coal. The gray ramp appears at the boundary of different components during imaging. 10 representative coal samples from different rank in China were selected, and the edge detection of the sample false boundary was extracted by Prewitt operator. The pixel of the false boundary transition zone was 10%-27% of the image of total coal particle. Compared with the standard results of manual identification done by domestic authoritative experts in coal petrology field, the results show that the deviation of the vitrinite, inertinite and liptinite group with the removal of false boundary is much lower than that with false boundary, the former is closer to manual identification result.

Keywords

microscopic image, maceral group, false boundary, gray ramp, edge detection, automatic image recognition

DOI

10.3969/j.issn.1001-1986.2019.06.008

Reference

[1] MCCARTNEY J T,O'DONNELL J J,ERGUN S. Determination of proportions of coal components by automated microscopic reflectance scanning[J]. Fuel,1971,50:226-235.

[2] DAVIS A,FRANCIS J V. Developments in automated reflectance microscopy of coal[J]. Journal of Microscopy,1977,109(1):3-12.

[3] DAVIS A,KUEHN K W,MAYLOTTE D H,et al. Mapping of polished coal surfaces by automated reflectance microscopy[J]. Journal of Microscopy,1983,132(3):297-302.

[4] 姚伯元,许国贤. 煤/焦反射率自动测定系统与技术方法[J]. 煤田地质与勘探,1996,24(4):18-21. YAO Boyuan,XU Guoxian. The tool and technical method for measuring coal/coke reflectance[J]. Coal Geology & Exploration,1996,24(4):18-21.

[5] ENGLAND B M,MIKKA R A,BAGNALL E J. Petrographic characterization of coal using automatic[J]. Journal of Microscopy,1979,116(3):329-336.

[6] CHAO E C T,MINKIN J A, THOMPSON C L. Application of automated image analysis to coal petrography[J]. International Journal of Coal Geology,1982,2:113-150.

[7] WOLFGANG R,MONIKA S. Characterization of coal and coal blends by automatic image analysis[J]. Fuel,1984,63:313-317.

[8] 金奎励,夏俭. 煤组分组定量与镜质组反射率测定的自动化测试[J]. 中国矿业学院学报,1986(1):60-67. JIN Kuili,XIA Jian. The automated coal petrology in determining maceral group composition and the reflectance of vitrinite[J]. Journal of China University of Mining & Technology,1986(1):60-67.

[9] GOODARZI F. The use of automated image analysis in coal petrology[J]. Earth Science,1987,24:1064-1069.

[10] PEARSON D E. Probability analysis of blended coking coals[J]. International Journal of Coal Geology,1991,19:109-119.

[11] PRATT K C. The use of composite and mosaic imaging of polished surfaces to enhance petrographic analysis of coal by image[J]. Organic Geochemistry,1993,20(6):759-768.

[12] O'BRIEN G,JENKINS B,ESTERLE J,et al. Coal characterization by automated coal petrography[J]. Fuel,2003,82:1067-1073.

[13] 王素婷,朱宪坤,吕青. 基于RILBP-GLCM算法的煤岩显微组分识别[J]. 煤炭学报,2017,42(3):142-144. WANG Suting,ZHU Xiankun,LYU Qing. Coal rock macerals recognition based on RILBP-GLCM algorithm[J]. Journal of China Coal Society,2017,42(3):142-144.

[14] 王文韬,胡德生,尹文义,等. 数字化煤岩分析系统的设计与实现[J]. 中国图像图形学报,2003,8(7):783-787. WANG Wentao,HU Desheng,YIN Wenyi,et al. Design and implementation of digital coal petrography analysis system[J]. Journal of Image and Graphics,2003,8(7):783-787.

[15] 胡德生,王文韬,刘其真. 数字化自动煤岩分析技术的开发[J]. 钢铁,2005,40(7):17-21. HU Desheng,WANG Wentao,LIU Qizhen. Development of digital automatically analysis technique for maceral specification[J]. Iron and Steel,2005,40(7):17-21.

[16] 陈洪博,白向飞,李振涛,等. 图像法测定煤岩组分反射率工作曲线的建立与应用[J]. 煤炭学报,2014,39(3):562-567. CHEN Hongbo,BAI Xiangfei,LI Zhentao,et al. Working curve establishing and application of determining maceral reflectance by image analysis method[J]. Journal of China Coal Society,2014,39(3):562-567.

[17] LESTER E,ALLEN M,CLOKE M,et al. An automated image analysis system for major maceral group analysis in coals[J]. Fuel,1994,73(11):1729-1734.

[18] CLOKE M,LESTER E,ALLEN M,et al. Repeatability of maceral analysis using image analysis systems[J]. Fuel,1995,74(5):654-658.

[19] LESTER E,WATTS D,CLOKE M. A novel automated image analysis method for maceral analysis[J]. Fuel,2002,81:2209-2217.

[20] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. 煤的显微组分组和矿物测定方法:GB/T 8899-2013[S]. 北京:中国标准出版社,2014.

[21] 杨起,韩德馨. 中国煤田地质学(上册)[M]. 北京:煤炭工业出版社,1980:22-44.

[22] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. 烟煤显微组分分类:GB/T 15588-2013[S]. 北京:中国标准出版社,2014.

[23] GONZALEZ R C,WOODS R E. 数字图像处理. 阮秋琦等,译[M]. 北京:电子工业出版社,2011:449-467.

[24] 张秀仪,龚至从,门桂珍,等. 烟煤的成因-工业分类[J]. 煤炭学报,1981(2):30-37. ZHANG Xiuyi,GONG Zhicong,MEN Guizhen,et al. Genetic-technical classification of bituminous coal[J]. Journal of China Coal Society,1981(2):30-37.

[25] 陈鹏. 中国煤炭性质、分类和利用[M]. 北京:化学工业出版社,2001:184-190.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.